def demo(args):
#
model = STELA(backbone=args.backbone, num_classes=2)
model.load_state_dict(torch.load(args.weights))
model.eval()
ims_list = [x for x in os.listdir(args.ims_dir) if is_image(x)]
for _, im_name in enumerate(ims_list):
im_path = os.path.join(args.ims_dir, im_name)
src = cv2.imread(im_path, cv2.IMREAD_COLOR)
im = cv2.cvtColor(src, cv2.COLOR_BGR2RGB)
cls_dets = im_detect(model, im, target_sizes=args.target_size)
for j in range(len(cls_dets)):
cls, scores = cls_dets[j, 0], cls_dets[j, 1]
bbox = cls_dets[j, 2:]
if len(bbox) == 4:
draw_caption(src, bbox, '{:1.3f}'.format(scores))
cv2.rectangle(src, (int(bbox[0]), int(bbox[1])),
(int(bbox[2]), int(bbox[3])),
color=(0, 0, 255),
thickness=2)
else:
pts = np.array([rbox_2_quad(bbox[:5]).reshape((4, 2))],
dtype=np.int32)
cv2.drawContours(src, pts, 0, color=(0, 255, 0), thickness=2)
# display original anchors
# if len(bbox) > 5:
# pts = np.array([rbox_2_quad(bbox[5:]).reshape((4, 2))], dtype=np.int32)
# cv2.drawContours(src, pts, 0, color=(0, 0, 255), thickness=2)
# resize for better shown
im = cv2.resize(src, (800, 800), interpolation=cv2.INTER_LINEAR)
cv2.imshow('Detection Results', im)
cv2.waitKey(0)
def demo(args):
hyps = hyp_parse(args.hyp)
ds = DATASETS[args.dataset](level=1)
model = RetinaNet(backbone=args.backbone, hyps=hyps)
if args.weight.endswith('.pth'):
chkpt = torch.load(args.weight)
# load model
if 'model' in chkpt.keys():
model.load_state_dict(chkpt['model'])
else:
model.load_state_dict(chkpt)
print('load weight from: {}'.format(args.weight))
model.eval()
# if os.path.exists('outputs'):
# shutil.rmtree('outputs')
# os.mkdir('outputs')
t0 = time.time()
if not args.dataset == 'DOTA':
ims_list = [x for x in os.listdir(args.ims_dir) if is_image(x)]
for idx, im_name in enumerate(ims_list):
s = ''
t = time.time()
im_path = os.path.join(args.ims_dir, im_name)
s += 'image %g/%g %s: ' % (idx, len(ims_list), im_path)
src = cv2.imread(im_path, cv2.IMREAD_COLOR)
im = cv2.cvtColor(src, cv2.COLOR_BGR2RGB)
cls_dets = im_detect(model, im, target_sizes=args.target_size)
for j in range(len(cls_dets)):
cls, scores = cls_dets[j, 0], cls_dets[j, 1]
bbox = cls_dets[j, 2:]
if len(bbox) == 4:
draw_caption(src, bbox, '{:1.3f}'.format(scores))
cv2.rectangle(src, (int(bbox[0]), int(bbox[1])),
(int(bbox[2]), int(bbox[3])),
color=(0, 0, 255),
thickness=2)
else:
pts = np.array([rbox_2_quad(bbox[:5]).reshape((4, 2))],
dtype=np.int32)
cv2.drawContours(src,
pts,
0,
color=(0, 255, 0),
thickness=2)
put_label = True
if put_label:
label = ds.return_class(cls) + str(' %.2f' % scores)
fontScale = 0.7
font = cv2.FONT_HERSHEY_COMPLEX
thickness = 1
t_size = cv2.getTextSize(label,
font,
fontScale=fontScale,
thickness=thickness)[0]
c1 = tuple(bbox[:2].astype('int'))
c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 5
cv2.rectangle(src, c1, c2, [0, 255, 0], -1) # filled
cv2.putText(src,
label, (c1[0], c1[1] - 5),
font,
fontScale, [0, 0, 0],
thickness=thickness,
lineType=cv2.LINE_AA)
# display original anchors
# if len(bbox) > 5:
# pts = np.array([rbox_2_quad(bbox[5:]).reshape((4, 2))], dtype=np.int32)
# cv2.drawContours(src, pts, 0, color=(0, 0, 255), thickness=2)
# resize for better shown
# im = cv2.resize(src, (800, 800), interpolation=cv2.INTER_LINEAR)
# cv2.imshow('Detection Results', im)
# cv2.waitKey(0)
print('%sDone. (%.3fs) %d objs' %
(s, time.time() - t, len(cls_dets)))
# save image
out_path = os.path.join('outputs', os.path.split(im_path)[1])
cv2.imwrite(out_path, src)
## DOTA detct on large image
else:
evaluate(args.target_size,
args.ims_dir,
'DOTA',
args.backbone,
args.weight,
hyps=hyps,
conf=0.05)
if os.path.exists('outputs/dota_out'):
shutil.rmtree('outputs/dota_out')
os.mkdir('outputs/dota_out')
exec(
'cd outputs && rm -rf detections && rm -rf integrated && rm -rf merged'
)
ResultMerge('outputs/detections', 'outputs/integrated',
'outputs/merged', 'outputs/dota_out')
img_path = os.path.join(args.ims_dir, 'images')
label_path = 'outputs/dota_out'
save_imgs = False
if save_imgs:
show_dota_results(img_path, label_path)
print('Done. (%.3fs)' % (time.time() - t0))
def demo(backbone='eb2',
weights='weights/deploy_eb_ship_15.pth',
ims_dir='sample',
target_size=768):
#
model = STELA(backbone=backbone, num_classes=2)
model.load_state_dict(torch.load(weights))
# model.eval()
# print(model)
classifier = EfficientNet.from_name(net_name)
num_ftrs = classifier._fc.in_features
classifier._fc = nn.Linear(num_ftrs, class_num)
classifier = classifier.cuda()
best_model_wts = 'dataset/weismoke/model/efficientnet-b0.pth'
classifier.load_state_dict(torch.load(best_model_wts))
ims_list = [x for x in os.listdir(ims_dir) if is_image(x)]
import shutil
shutil.rmtree('output/')
os.mkdir('output/')
for _, im_name in enumerate(ims_list):
im_path = os.path.join(ims_dir, im_name)
src = cv2.imread(im_path, cv2.IMREAD_COLOR)
im = cv2.cvtColor(src, cv2.COLOR_BGR2RGB)
import time
# start=time.clock()
cls_dets = im_detect(model, im, target_sizes=target_size)
end = time.clock()
# print('********time*********',end-start)
# val='/home/jd/projects/haha/chosename/val_plane_split/label_new/'
"""
if(len(cls_dets)==0):
print('*********no********',im_name)
#image_path = os.path.join(img_path, name + ext) #样本图片的名称
shutil.move(val+im_name[0:-4]+'.txt', 'hard') #移动该样本图片到blank_img_path
shutil.move(im_path, 'hard/') #移动该样本图片的标签到blank_label_path
continue
"""
fw = open('output/' + im_name[:-4] + '.txt', 'a')
fw.truncate()
for j in range(len(cls_dets)):
cls, scores = cls_dets[j, 0], cls_dets[j, 1]
# print ('cls,score',cls,scores)
bbox = cls_dets[j, 2:]
# print(bbox)
if len(bbox) == 4:
draw_caption(src, bbox, '{:1.3f}'.format(scores))
cv2.rectangle(src, (int(bbox[0]), int(bbox[1])),
(int(bbox[2]), int(bbox[3])),
color=(0, 0, 255),
thickness=2)
else:
pts = np.array([rbox_2_quad(bbox[:5]).reshape((4, 2))],
dtype=np.int32)
# print('####pts####',pts)
cv2.drawContours(src, pts, 0, color=(0, 255, 0), thickness=2)
# display original anchors
# if len(bbox) > 5:
# pts = np.array([rbox_2_quad(bbox[5:]).reshape((4, 2))], dtype=np.int32)
# cv2.drawContours(src, pts, 0, color=(0, 0, 255), thickness=2)
patch = crop_image(im, pts)
pred = classify(classifier, patch)
fw.write(
str(pts.flatten()[0]) + ' ' + str(pts.flatten()[1]) + ' ' +
str(pts.flatten()[2]) + ' ' + str(pts.flatten()[3]) + ' ' +
str(pts.flatten()[4]) + ' ' + str(pts.flatten()[5]) + ' ' +
str(pts.flatten()[6]) + ' ' + str(pts.flatten()[7]) + ' ' +
classes[pred] + '\n')
fw.close()
# resize for better shown
im = cv2.resize(src, (768, 768), interpolation=cv2.INTER_LINEAR)
cv2.imwrite('output_img/' + im_name, im)
train_img_dir = '/home/jd/projects/bifpn/sample_plane/'
groundtruth_txt_dir = '/home/jd/projects/haha/chosename/val_plane_split/label_new/'
detect_txt_dir = '/home/jd/projects/bifpn/output/'
Recall, Precision, mAP = compute_acc(train_img_dir, groundtruth_txt_dir,
detect_txt_dir)
print('*******', Recall)
return Recall, Precision, mAP
